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neurodegenerative diseases and iPSC-derived neurons representative of the disease. Testing promising compounds for their in vivo pharmacokinetic properties in wildtype and mouse models for neurodegenerative diseases
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, imaging informatics, and large language models (LLM) is preferred. This position will require collaboration with diverse stakeholders, including informatics experts, clinicians, basic scientists, and
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. Experience in the following areas would be preferred. soil health indexing mineral associated organic matter (MAOM) data-driven machine learning models and simulation modeling Special Instructions
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collaboration with the UF Artificial Intelligence initiative. The successful candidate will have the opportunity to work on cutting-edge projects aimed at building large-scale models for neuroimaging and
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to those who have expertise in neuroanatomy, small animal surgery, electrophysiology and behavioral pharmacology. Prior working experience with rodent models. Small animal stereotaxic surgery experience
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Development (50%): Develop and apply advanced statistical modeling and coding skills (primarily in R) to build a QMRA framework using data from both the scientific literature and those collected from
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to contribute to research in concentrating solar thermal (CST) and solar fuels. Key Responsibilities: • Conduct research in CST and solar fuels, focusing on thermochemical reaction modeling and experimentation
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postdoctoral associate positions, starting immediately. Dr. Liu has extensive experience in big data analytics, systems biology, probabilistic graphical models, causal inference and machine learning. Dr. Liu's
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ability to analyze large datasets Knowledge of coastal and nearshore processes Preferred Qualifications: Proficiency in statistical modeling and time series analysis Experience with machine learning or deep
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animal models and extending these insights to understanding and intervening in human diseases. Successful candidates will be self-motivated scientists, with a passion for advancing cutting-edge research